Predictors of Early Job Turnover Among Juvenile Correctional Facility Staff
Bibliographic record
Abstract
Although there is minimal research on job turnover among staff working in juvenile correctional facilities, turnover continues to be a costly problem with far reaching ramifications. This study examined 12-month turnover (operationalized in terms of resignations) among 13 successive cohorts of 475 staff who completed a basic training academy over a 3-year period. Turnover approached one quarter and was most probable during the first 6 months following academy completion. Seven demographic and nine work-related predictors were analyzed, including measures of job satisfaction and organizational commitment. Only one variable predicted turnover. Turnover was significantly less likely among staff displaying satisfaction with coworkers. The findings imply that turnover among newer employees might be curtailed through programs such as staff mentoring implemented during the first 6 to 9 months of a new employee’s tenure to foster positive coworker relations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".